Finding the right fit: chiseling structures out of cryo-electron microscopy maps

Structural Analysis of Protein Complexes by Cryo-Electron Microscopy

Structural studies of bio-complexes using single particle cryo-Electron Microscopy (cryo-EM) is nowadays a well-established technique in structural biology and has become competitive with X-ray crystallography. Development of digital registration systems for electron microscopy images and algorithms for the fast and efficient processing of the recorded images and their following analysis has facilitated the determination of structures at near-atomic resolution. The latest advances in EM have enabled the determination of protein complex structures at 1.4-3 Å resolution for an extremely broad range of sizes (from ~100 kDa up to hundreds of MDa (Bartesaghi et al., Science 348(6239):1147-1151, 2015; Herzik et al., Nat Commun 10:1032, 2019; Wu et al., J Struct Biol X 4:100020, 2020; Zhang et al., Nat Commun 10:5511, 2019; Zhang et al., Cell Res 30(12):1136-1139, 2020; Yip et al., Nature 587(7832):157-161, 2020; https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year )). In 2022, nearly 1200 structures deposited to the EMDB database were at a resolution of better than 3 Å ( https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year ).To date, the highest resolutions have been achieved for apoferritin, which comprises a homo-oligomer of high point group symmetry (O432) and has rigid organization together with high stability (Zhang et al., Cell Res 30(12):1136-1139, 2020; Yip et al., Nature 587(7832):157-161, 2020). It has been used as a test object for the assessments of modern cryo-microscopes and processing methods during the last 5 years. In contrast to apoferritin bacterial secretion systems are typical examples of multi protein complexes exhibiting high flexibility owing to their functions relating to the transportation of small molecules, proteins, and DNA into the extracellular space or target cells. This makes their structural characterization extremely challenging (Barlow, Methods Mol Biol 532:397-411, 2009; Costa et al., Nat Rev Microbiol 13:343-359, 2015). The most feasible approach to reveal their spatial organization and functional modification is cryo-electron microscopy (EM). During the last decade, structural cryo-EM has become broadly used for the analysis of the bio-complexes that comprise multiple components and are not amenable to crystallization (Lyumkis, J Biol Chem 294:5181-5197, 2019; Orlova and Saibil, Methods Enzymol 482:321-341, 2010; Orlova and Saibil, Chem Rev 111(12):7710-7748, 2011).In this review, we will describe the basics of sample preparation for cryo-EM, the principles of digital data collection, and the logistics of image analysis focusing on the common steps required for reconstructions of both small and large biological complexes together with refinement of their structures to nearly atomic resolution. The workflow of processing will be illustrated by examples of EM analysis of Type IV Secretion System.

Bayesian methods in integrative structure modeling

There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.

Harris S, Ramanathan A. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Structural basis of SARS-CoV-2 spike protein induced by ACE2

Motivation

The recent emergence of the novel SARS-coronavirus 2 (SARS-CoV-2) and its international spread pose a global health emergency. The spike (S) glycoprotein binds ACE2 and promotes SARS-CoV-2 entry into host cells. The trimeric S protein binds the receptor using the receptor-binding domain (RBD) causing conformational changes in S protein that allow priming by host cell proteases. Unraveling the dynamic structural features used by SARS-CoV-2 for entry might provide insights into viral transmission and reveal novel therapeutic targets. Using structures determined by X-ray crystallography and cryo-EM, we performed structural analysis and atomic comparisons of the different conformational states adopted by the SARS-CoV-2-RBD.

Results

Here, we determined the key structural components induced by the receptor and characterized their intramolecular interactions. We show that κ-helix (polyproline-II) is a predominant structure in the binding interface and in facilitating the conversion to the active form of the S protein. We demonstrate a series of conversions between switch-like κ-helix and β-strand, and conformational variations in a set of short α-helices which affect the hinge region. These conformational changes lead to an alternating pattern in conserved disulfide bond configurations positioned at the hinge, indicating a possible disulfide exchange, an important allosteric switch implicated in viral entry of various viruses, including HIV and murine coronavirus. The structural information presented herein enables to inspect and understand the important dynamic features of SARS-CoV-2-RBD and propose a novel potential therapeutic strategy to block viral entry. Overall, this study provides guidance for the design and optimization of structure-based intervention strategies that target SARS-CoV-2.

Availability

We have implemented the proposed methods in an R package freely available at https://github.com/Grantlab/bio3d

Supplementary information

Supplementary data are available at Bioinformatics online.

Theoretical and practical approaches to improve the performance of local correlation algorithms for volume data analysis and shape recognition

Several approaches are presented to improve the performance of local correlation algorithms based on prior information about 3D search and target maps.

In this paper, several approaches to be used to accelerate algorithms for fitting an atomic structure into a given 3D density map determined by cryo-EM are discussed. Rotation and translation of the atomic structure to find similarity scores are used and implemented with discrete Fourier transforms. Several rotations can be combined into groups to accelerate processing. The finite resolution of experimental and simulated maps allows a reduction in the number of rotations and translations needed in order to estimate similarity-score values.

Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution

Structure determination using cryo-electron microscopy (cryo-EM) medium-resolution density maps is often facilitated by flexible fitting. Avoiding overfitting, adjusting force constants driving the structure to the density map, and emulating complex conformational transitions are major concerns in the fitting. To address them, we develop a new method based on a three-step multi-scale protocol. First, flexible fitting molecular dynamics (MD) simulations with coarse-grained structure-based force field and replica-exchange scheme between different force constants replicas are performed. Second, fitted Cα atom positions guide the all-atom structure in targeted MD. Finally, the all-atom flexible fitting refinement in implicit solvent adjusts the positions of the side chains in the density map. Final models obtained via the multi-scale protocol are significantly better resolved and more reliable in comparison with long all-atom flexible fitting simulations. The protocol is useful for multi-domain systems with intricate structural transitions as it preserves the secondary structure of single domains.

A primer on resolving the nanoscale structure of the plasma membrane with light and electron microscopy

Taraska reviews the imaging methods that are being used to understand the structure of the plasma membrane at the molecular level.

The plasma membrane separates a cell from its external environment. All materials and signals that enter or leave the cell must cross this hydrophobic barrier. Understanding the architecture and dynamics of the plasma membrane has been a central focus of general cellular physiology. Both light and electron microscopy have been fundamental in this endeavor and have been used to reveal the dense, complex, and dynamic nanoscale landscape of the plasma membrane. Here, I review classic and recent developments in the methods used to image and study the structure of the plasma membrane, particularly light, electron, and correlative microscopies. I will discuss their history and use for mapping the plasma membrane and focus on how these tools have provided a structural framework for understanding the membrane at the scale of molecules. Finally, I will describe how these studies provide a roadmap for determining the nanoscale architecture of other organelles and entire cells in order to bridge the gap between cellular form and function.

2DHybrid Analysis

We have developed an approach termed '2D hybrid analysis' for building three-dimensional (3D) structures from electron microscopy (EM) images of biological molecules. The key advantage is that it is applicable to flexible molecules, which are difficult to analyze by the approach in which 3DEM maps are reconstructed. In the proposed approach, a large number of atomic models with different conformations are first built by computer simulation. Then, simulated EM images are produced from each atomic model. Finally, these images are compared with an experimental EM image to identify the best-fitting atomic model. Two kinds of models are used to simulate the EM images: the negative-stain model and the simple projection model. Although the former is more realistic, the latter permits faster computation. We applied this approach to the averaged EM images of integrin. Although many of these were reproduced well by the best-fitting atomic models, others did not closely resemble any of the simulated EM images. However, the latter group were well reproduced by averaging multiple simulated EM images originating from atomic models with rather different conformations or orientations. This indicated that our approach is capable of detecting mixtures of conformations in the averaged EM images, which should assist in their correct interpretation.

GROmaρs: A GROMACS-Based Toolset to Analyze Density Maps Derived from Molecular Dynamics Simulations

We introduce a computational toolset, named GROmaρs, to obtain and compare time-averaged density maps from molecular dynamics simulations. GROmaρs efficiently computes density maps by fast multi-Gaussian spreading of atomic densities onto a three-dimensional grid. It complements existing map-based tools by enabling spatial inspection of atomic average localization during the simulations. Most importantly, it allows the comparison between computed and reference maps (e.g., experimental) through calculation of difference maps and local and time-resolved global correlation. These comparison operations proved useful to quantitatively contrast perturbed and control simulation data sets and to examine how much biomolecular systems resemble both synthetic and experimental density maps. This was especially advantageous for multimolecule systems in which standard comparisons like RMSDs are difficult to compute. In addition, GROmaρs incorporates absolute and relative spatial free-energy estimates to provide an energetic picture of atomistic localization. This is an open-source GROMACS-based toolset, thus allowing for static or dynamic selection of atoms or even coarse-grained beads for the density calculation. Furthermore, masking of regions was implemented to speed up calculations and to facilitate the comparison with experimental maps. Beyond map comparison, GROmaρs provides a straightforward method to detect solvent cavities and average charge distribution in biomolecular systems. We employed all these functionalities to inspect the localization of lipid and water molecules in aquaporin systems, the binding of cholesterol to the G protein coupled chemokine receptor type 4, and the identification of permeation pathways through the dermicidin antimicrobial channel. Based on these examples, we anticipate a high applicability of GROmaρs for the analysis of molecular dynamics simulations and their comparison with experimentally determined densities.

Acceleration of cryo-EM Flexible Fitting for Large Biomolecular Systems by Efficient Space Partitioning

Flexible fitting is a powerful technique to build the 3D structures of biomolecules from cryoelectron microscopy (cryo-EM) density maps. One popular method is a cross-correlation coefficient-based approach, where the molecular dynamics (MD) simulation is carried out with the biasing potential that includes the cross-correlation coefficient between the experimental and simulated density maps. Here, we propose efficient parallelization schemes for the calculation of the cross-correlation coefficient to accelerate flexible fitting. Our schemes are tested for small, medium, and large biomolecules using CPU and hybrid CPU + GPU architectures. The scheme for the atomic decomposition MD is suitable for small proteins such as Ca²⁺-ATPase with the all-atom Go model, while that for the domain decomposition MD is better for larger systems such as ribosome with the all-atom Go or the all-atom explicit solvent models. Our methods allow flexible fitting for various biomolecules with reasonable computational cost. This approach also connects high-resolution structure refinements with investigation of protein structure-function relationship.

CryoEM-based hybrid modeling approaches for structure determination

Recent advances in cryo-electron microscopy (cryoEM) have dramatically improved the resolutions at which vitrified biological specimens can be studied, revealing new structural and mechanistic insights over a broad range of spatial scales. Bolstered by these advances, much effort has been directed toward the development of hybrid modeling methodologies for the construction and refinement of high-fidelity atomistic models from cryoEM data. In this brief review, we will survey the key elements of cryoEM-based hybrid modeling, providing an overview of available computational tools and strategies as well as several recent applications.

Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.

Tools for the cryo-EM gold rush: going from the cryo-EM map to the atomistic model

As cryo-electron microscopy (cryo-EM) enters mainstream structural biology, the demand for fitting methods is high. Here, we review existing flexible fitting methods for cryo-EM. We discuss their importance, potential concerns and assessment strategies. We aim to give readers concrete descriptions of cryo-EM flexible fitting methods with corresponding examples.

A fragment based method for modeling of protein segments into cryo-EM density maps

BACKGROUND

Single-particle analysis of electron cryo-microscopy (cryo-EM) is a key technology for elucidation of macromolecular structures. Recent technical advances in hardware and software developments significantly enhanced the resolution of cryo-EM density maps and broadened the applicability and the circle of users. To facilitate modeling of macromolecules into cryo-EM density maps, fast and easy to use methods for modeling are now demanded.

RESULTS

Here we investigated and benchmarked the suitability of a classical and well established fragment-based approach for modeling of segments into cryo-EM density maps (termed FragFit). FragFit uses a hierarchical strategy to select fragments from a pre-calculated set of billions of fragments derived from structures deposited in the Protein Data Bank, based on sequence similarly, fit of stem atoms and fit to a cryo-EM density map. The user only has to specify the sequence of the segment and the number of the N- and C-terminal stem-residues in the protein. Using a representative data set of protein structures, we show that protein segments can be accurately modeled into cryo-EM density maps of different resolution by FragFit. Prediction quality depends on segment length, the type of secondary structure of the segment and local quality of the map.

CONCLUSION

Fast and automated calculation of FragFit renders it applicable for implementation of interactive web-applications e.g. to model missing segments, flexible protein parts or hinge-regions into cryo-EM density maps.

Flexible Fitting of Atomic Models into Cryo-EM Density Maps Guided by Helix Correspondences

Although electron cryo-microscopy (cryo-EM) has recently achieved resolutions of better than 3 Å, at which point molecular modeling can be done directly from the density map, analysis and annotation of a cryo-EM density map still primarily rely on fitting atomic or homology models to the density map. In this article, we present, to our knowledge, a new method for flexible fitting of known or modeled protein structures into cryo-EM density maps. Unlike existing methods that are guided by local density gradients, our method is guided by correspondences between the α-helices in the density map and model, and does not require an initial rigid-body fitting step. Compared with current methods on both simulated and experimental density maps, our method not only achieves greater accuracy for proteins with large deformations but also runs as fast or faster than many of the other flexible fitting routines.

Integrative modelling of cellular assemblies

A wide variety of experimental techniques can be used for understanding the precise molecular mechanisms underlying the activities of cellular assemblies. The inherent limitations of a single experimental technique often requires integration of data from complementary approaches to gain sufficient insights into the assembly structure and function. Here, we review popular computational approaches for integrative modelling of cellular assemblies, including protein complexes and genomic assemblies. We provide recent examples of integrative models generated for such assemblies by different experimental techniques, especially including data from 3D electron microscopy (3D-EM) and chromosome conformation capture experiments, respectively. We highlight general concepts in integrative modelling and discuss the need for careful formulation and merging of different types of information.

Improved metrics for comparing structures of macromolecular assemblies determined by 3D electron-microscopy

Recent developments in 3-dimensional electron microcopy (3D-EM) techniques and a concomitant drive to look at complex molecular structures, have led to a rapid increase in the amount of volume data available for biomolecules. This creates a demand for better methods to analyse the data, including improved scores for comparison, classification and integration of data at different resolutions. To this end, we developed and evaluated a set of scoring functions that compare 3D-EM volumes. To test our scores we used a benchmark set of volume alignments derived from the Electron Microscopy Data Bank. We find that the performance of different scores vary with the map-type, resolution and the extent of overlap between volumes. Importantly, adding the overlap information to the local scoring functions can significantly improve their precision and accuracy in a range of resolutions. A combined score involving the local mutual information and overlap (LMI_OV) performs best overall, irrespective of the map category, resolution or the extent of overlap, and we recommend this score for general use. The local mutual information score itself is found to be more discriminatory than cross-correlation coefficient for intermediate-to-low resolution maps or when the map size and density distribution differ significantly. For comparing map surfaces, we implemented two filters to detect the surface points, including one based on the 'extent of surface exposure'. We show that scores that compare surfaces are useful at low resolutions and for maps with evident surface features. All the scores discussed are implemented in TEMPy (http://tempy.ismb.lon.ac.uk/).

2D hybrid analysis: Approach for building three-dimensional atomic model by electron microscopy image matching

In this study, we develop an approach termed “2D hybrid analysis” for building atomic models by image matching from electron microscopy (EM) images of biological molecules. The key advantage is that it is applicable to flexible molecules, which are difficult to analyze by 3DEM approach. In the proposed approach, first, a lot of atomic models with different conformations are built by computer simulation. Then, simulated EM images are built from each atomic model. Finally, they are compared with the experimental EM image. Two kinds of models are used as simulated EM images: the negative stain model and the simple projection model. Although the former is more realistic, the latter is adopted to perform faster computations. The use of the negative stain model enables decomposition of the averaged EM images into multiple projection images, each of which originated from a different conformation or orientation. We apply this approach to the EM images of integrin to obtain the distribution of the conformations, from which the pathway of the conformational change of the protein is deduced.

Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps

A growing array of experimental techniques allows us to characterize the three-dimensional structure of large biological assemblies at increasingly higher resolution. In addition to X-ray crystallography and nuclear magnetic resonance in solution, new structure determination methods such cryo-electron microscopy (cryo-EM), crosslinking/mass spectrometry and solid-state NMR have emerged. Often it is not sufficient to use a single experimental method, but complementary data need to be collected by using multiple techniques. The integration of all datasets can only be achieved by computational means. This article describes Inferential structure determination, a Bayesian approach to integrative modeling of biomolecular complexes with hybrid structural data. I will introduce probabilistic models for cryo-EM maps and outline Markov chain Monte Carlo algorithms for sampling model structures from the posterior distribution. I will focus on rigid and flexible modeling with cryo-EM data and discuss some of the computational challenges of Bayesian inference in the context of biomolecular modeling.

Variability of Protein Structure Models from Electron Microscopy

An increasing number of biomolecular structures are solved by electron microscopy (EM). However, the quality of structure models determined from EM maps vary substantially. To understand to what extent structure models are supported by information embedded in EM maps, we used two computational structure refinement methods to examine how much structures can be refined using a dataset of 49 maps with accompanying structure models. The extent of structure modification as well as the disagreement between refinement models produced by the two computational methods scaled inversely with the global and the local map resolutions. A general quantitative estimation of deviations of structures for particular map resolutions are provided. Our results indicate that the observed discrepancy between the deposited map and the refined models is due to the lack of structural information present in EM maps and thus these annotations must be used with caution for further applications.

Use of evolutionary information in the fitting of atomic level protein models in low resolution cryo-EM map of a protein assembly improves the accuracy of the fitting

Protein-protein interface residues, especially those at the core of the interface, exhibit higher conservation than residues in solvent exposed regions. Here, we explore the ability of this differential conservation to evaluate fittings of atomic models in low-resolution cryo-EM maps and select models from the ensemble of solutions that are often proposed by different model fitting techniques. As a prelude, using a non-redundant and high-resolution structural dataset involving 125 permanent and 95 transient complexes, we confirm that core interface residues are conserved significantly better than nearby non-interface residues and this result is used in the cryo-EM map analysis. From the analysis of inter-component interfaces in a set of fitted models associated with low-resolution cryo-EM maps of ribosomes, chaperones and proteasomes we note that a few poorly conserved residues occur at interfaces. Interestingly a few conserved residues are not in the interface, though they are close to the interface. These observations raise the potential requirement of refitting the models in the cryo-EM maps. We show that sampling an ensemble of models and selection of models with high residue conservation at the interface and in good agreement with the density helps in improving the accuracy of the fit. This study indicates that evolutionary information can serve as an additional input to improve and validate fitting of atomic models in cryo-EM density maps.

De Novo modeling in cryo-EM density maps with Pathwalking

As electron cryo-microscopy (cryo-EM) can now frequently achieve near atomic resolution, accurate interpretation of these density maps in terms of atomistic detail has become paramount in deciphering macromolecular structure and function. However, there are few software tools for modeling protein structure from cryo-EM density maps in this resolution range. Here, we present an extension of our original Pathwalking protocol, which can automatically trace a protein backbone directly from a near-atomic resolution (3-6Å) density map. The original Pathwalking approach utilized a Traveling Salesman Problem solver for backbone tracing, but manual adjustment was still required during modeling. In the new version, human intervention is minimized and we provide a more robust approach for backbone modeling. This includes iterative secondary structure identification, termini detection and the ability to model multiple subunits without prior segmentation. Overall, the new Pathwalking procedure provides a more complete and robust tool for annotating protein structure function in near-atomic resolution density maps.

Refinement of atomic models in high resolution EM reconstructions using Flex-EM and local assessment

As the resolutions of Three Dimensional Electron Microscopic reconstructions of biological macromolecules are being improved, there is a need for better fitting and refinement methods at high resolutions and robust approaches for model assessment. Flex-EM/MODELLER has been used for flexible fitting of atomic models in intermediate-to-low resolution density maps of different biological systems. Here, we demonstrate the suitability of the method to successfully refine structures at higher resolutions (2.5-4.5Å) using both simulated and experimental data, including a newly processed map of Apo-GroEL. A hierarchical refinement protocol was adopted where the rigid body definitions are relaxed and atom displacement steps are reduced progressively at successive stages of refinement. For the assessment of local fit, we used the SMOC (segment-based Manders' overlap coefficient) score, while the model quality was checked using the Qmean score. Comparison of SMOC profiles at different stages of refinement helped in detecting regions that are poorly fitted. We also show how initial model errors can have significant impact on the goodness-of-fit. Finally, we discuss the implementation of Flex-EM in the CCP-EM software suite.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

γ-TEMPy: Simultaneous Fitting of Components in 3D-EM Maps of Their Assembly Using a Genetic Algorithm

We have developed a genetic algorithm for building macromolecular complexes using only a 3D-electron microscopy density map and the atomic structures of the relevant components. For efficient sampling the method uses map feature points calculated by vector quantization. The fitness function combines a mutual information score that quantifies the goodness of fit with a penalty score that helps to avoid clashes between components. Testing the method on ten assemblies (containing 3–8 protein components) and simulated density maps at 10, 15, and 20 Å resolution resulted in identification of the correct topology in 90%, 70%, and 60% of the cases, respectively. We further tested it on four assemblies with experimental maps at 7.2–23.5 Å resolution, showing the ability of the method to identify the correct topology in all cases. We have also demonstrated the importance of the map feature-point quality on assembly fitting in the lack of additional experimental information.

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γ-TEMPy uses a genetic algorithm to fit multiple components into 3D-EM density maps

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The fitness score is a combination of a Mutual Information score and a clash penalty

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Efficient sampling is aided by using map feature points from vector quantization

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Native topologies for assemblies containing up to eight components can be predicted

EMDataBank unified data resource for 3DEM

Three-dimensional Electron Microscopy (3DEM) has become a key experimental method in structural biology for a broad spectrum of biological specimens from molecules to cells. The EMDataBank project provides a unified portal for deposition, retrieval and analysis of 3DEM density maps, atomic models and associated metadata (emdatabank.org). We provide here an overview of the rapidly growing 3DEM structural data archives, which include maps in EM Data Bank and map-derived models in the Protein Data Bank. In addition, we describe progress and approaches toward development of validation protocols and methods, working with the scientific community, in order to create a validation pipeline for 3DEM data.

Electron Microscopy and Image Processing: Essential Tools for Structural Analysis of Macromolecules

Macromolecular electron microscopy typically depicts the structures of macromolecular complexes ranging from ∼200 kDa to hundreds of MDa. The amount of specimen required, a few micrograms, is typically 100 to 1000 times less than needed for X-ray crystallography or nuclear magnetic resonance spectroscopy. Micrographs of frozen-hydrated (cryogenic) specimens portray native structures, but the original images are noisy. Computational averaging reduces noise, and three-dimensional reconstructions are calculated by combining different views of free-standing particles ("single-particle analysis"). Electron crystallography is used to characterize two-dimensional arrays of membrane proteins and very small three-dimensional crystals. Under favorable circumstances, near-atomic resolutions are achieved. For structures at somewhat lower resolution, pseudo-atomic models are obtained by fitting high-resolution components into the density. Time-resolved experiments describe dynamic processes. Electron tomography allows reconstruction of pleiomorphic complexes and subcellular structures and modeling of macromolecules in their cellular context. Significant information is also obtained from metal-coated and dehydrated specimens.

Structural and Computational Biology in the Design of Immunogenic Vaccine Antigens

Vaccination is historically one of the most important medical interventions for the prevention of infectious disease. Previously, vaccines were typically made of rather crude mixtures of inactivated or attenuated causative agents. However, over the last 10–20 years, several important technological and computational advances have enabled major progress in the discovery and design of potently immunogenic recombinant protein vaccine antigens. Here we discuss three key breakthrough approaches that have potentiated structural and computational vaccine design. Firstly, genomic sciences gave birth to the field of reverse vaccinology, which has enabled the rapid computational identification of potential vaccine antigens. Secondly, major advances in structural biology, experimental epitope mapping, and computational epitope prediction have yielded molecular insights into the immunogenic determinants defining protective antigens, enabling their rational optimization. Thirdly, and most recently, computational approaches have been used to convert this wealth of structural and immunological information into the design of improved vaccine antigens. This review aims to illustrate the growing power of combining sequencing, structural and computational approaches, and we discuss how this may drive the design of novel immunogens suitable for future vaccines urgently needed to increase the global prevention of infectious disease.

TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits

TEMPy is an object-oriented Python library that provides the means to validate density fits in electron microscopy reconstructions. This article highlights several features of particular interest for this purpose and includes some customized examples.

Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.

Validation methods for low-resolution fitting of atomic structures to electron microscopy data

Fitting of atomic-resolution structures into reconstructions from electron cryo-microscopy is routinely used to understand the structure and function of macromolecular machines. Despite the fact that a plethora of fitting methods has been developed over recent years, standard protocols for quality assessment and validation of these fits have not been established. Here, we present the general concepts underlying current validation ideas as they relate to fitting of atomic-resolution models into electron cryo-microscopy reconstructions, with an emphasis on reconstructions with resolutions below the sub-nanometer range.

Tools for macromolecular model building and refinement into electron cryo-microscopy reconstructions

The recent rapid development of single-particle electron cryo-microscopy (cryo-EM) now allows structures to be solved by this method at resolutions close to 3 Å. Here, a number of tools to facilitate the interpretation of EM reconstructions with stereochemically reasonable all-atom models are described. The BALBES database has been repurposed as a tool for identifying protein folds from density maps. Modifications to Coot, including new Jiggle Fit and morphing tools and improved handling of nucleic acids, enhance its functionality for interpreting EM maps. REFMAC has been modified for optimal fitting of atomic models into EM maps. As external structural information can enhance the reliability of the derived atomic models, stabilize refinement and reduce overfitting, ProSMART has been extended to generate interatomic distance restraints from nucleic acid reference structures, and a new tool, LIBG, has been developed to generate nucleic acid base-pair and parallel-plane restraints. Furthermore, restraint generation has been integrated with visualization and editing in Coot, and these restraints have been applied to both real-space refinement in Coot and reciprocal-space refinement in REFMAC.